Among all relational operators the most difficult one to
process and optimize is the join. The
number of alternative plans to answer a query grows
exponentially with the number of joins included in it. Further
optimization effort is caused by the support of a variety of
join methods (e.g., nested loop, hash
join, merge join in PostgreSQL) to process individual joins
and a diversity of indexes (e.g.,
R-tree, B-tree, hash in PostgreSQL) as access paths for
relations.

The current PostgreSQL
optimizer implementation performs a near-exhaustive search over the space of
alternative strategies. This algorithm, first introduced in the
"System R" database, produces a
near-optimal join order, but can take an enormous amount of
time and memory space when the number of joins in the query
grows large. This makes the ordinary PostgreSQL query optimizer inappropriate
for queries that join a large number of tables.

The Institute of Automatic Control at the University of
Mining and Technology, in Freiberg, Germany, encountered the
described problems as its folks wanted to take the PostgreSQL DBMS as the backend for a
decision support knowledge based system for the maintenance of
an electrical power grid. The DBMS needed to handle large join
queries for the inference machine of the knowledge based
system.

Performance difficulties in exploring the space of possible
query plans created the demand for a new optimization technique
to be developed.

In the following we describe the implementation of a
Genetic Algorithm to solve the join
ordering problem in a manner that is efficient for queries
involving large numbers of joins.